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              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/installation.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/jetpack.html">Torch-TensorRT in JetPack</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/quick_start.html">Quick Start</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/capture_and_replay.html">Introduction</a></li>
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<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/torch_tensorrt_explained.html">Torch-TensorRT Explained</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/runtime.html">Deploying Torch-TensorRT Programs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/using_dla.html">DLA</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/mixed_precision.html">Compile Mixed Precision models with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/vgg16_ptq.html">Deploy Quantized Models using Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/engine_caching_example.html">Engine Caching</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/engine_caching_bert_example.html">Engine Caching (BERT)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/refit_engine_example.html">Refitting Torch-TensorRT Programs with New Weights</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/torch_export_cudagraphs.html">Torch Export with Cudagraphs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/converter_overloading.html">Overloading Torch-TensorRT Converters with Custom Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/auto_generate_converters.html">Automatically Generate a Converter for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/auto_generate_plugins.html">Automatically Generate a Plugin for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/weight_streaming_example.html">Weight Streaming</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/pre_allocated_output_example.html">Pre-allocated output buffer</a></li>
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<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../dynamo/torch_compile.html">TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
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<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/ptq.html">Post Training Quantization (PTQ)</a></li>
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<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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<p class="caption" role="heading"><span class="caption-text">Model Zoo</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../dynamo/torch_compile_resnet_example.html">Compiling ResNet with dynamic shapes using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/torch_compile_transformers_example.html">Compiling BERT using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/torch_compile_stable_diffusion.html">Compiling Stable Diffusion model using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../compile_hf_models.html">Compiling LLM models from Huggingface</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/torch_compile_gpt2.html">Compiling GPT2 using the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/torch_export_sam2.html">Compiling SAM2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/torch_export_flux_dev.html">Compiling FLUX.1-dev model using the Torch-TensorRT dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../notebooks.html">Legacy notebooks</a></li>
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<p class="caption" role="heading"><span class="caption-text">Python API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../py_api/ptq.html">torch_tensorrt.ts.ptq</a></li>
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<p class="caption" role="heading"><span class="caption-text">C++ API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
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<p class="caption" role="heading"><span class="caption-text">CLI Documentation</span></p>
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<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../contributors/dynamo_converters.html">Writing Dynamo Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../contributors/writing_dynamo_aten_lowering_passes.html">Writing Dynamo ATen Lowering Passes</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../contributors/ts_converters.html">Writing TorchScript Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../contributors/resource_management.html">Resource Management</a></li>
</ul>
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  <section id="serving-a-torch-tensorrt-model-with-triton">
<span id="serving-torch-tensorrt-with-triton"></span><span id="sphx-glr-tutorials-rendered-examples-triton"></span><h1>Serving a Torch-TensorRT model with Triton<a class="headerlink" href="#serving-a-torch-tensorrt-model-with-triton" title="Permalink to this heading">¶</a></h1>
<p>Optimization and deployment go hand in hand in a discussion about Machine
Learning infrastructure. Once network level optimization are done
to get the maximum performance, the next step would be to deploy it.</p>
<p>However, serving this optimized model comes with its own set of considerations
and challenges like: building an infrastructure to support concurrent model
executions, supporting clients over HTTP or gRPC and more.</p>
<p>The <a class="reference external" href="https://github.com/triton-inference-server/server">Triton Inference Server</a>
solves the aforementioned and more. Let’s discuss step-by-step, the process of
optimizing a model with Torch-TensorRT, deploying it on Triton Inference
Server, and building a client to query the model.</p>
<section id="step-1-optimize-your-model-with-torch-tensorrt">
<h2>Step 1: Optimize your model with Torch-TensorRT<a class="headerlink" href="#step-1-optimize-your-model-with-torch-tensorrt" title="Permalink to this heading">¶</a></h2>
<p>Most Torch-TensorRT users will be familiar with this step. For the purpose of
this demonstration, we will be using a ResNet50 model from Torchhub.</p>
<p>We will be working in the <code class="docutils literal notranslate"><span class="pre">//examples/triton</span></code> directory which contains the scripts used in this tutorial.</p>
<p>First pull the <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">NGC PyTorch Docker container</a>. You may need to create
an account and get the API key from <a class="reference external" href="https://ngc.nvidia.com/setup/">here</a>.
Sign up and login with your key (follow the instructions
<a class="reference external" href="https://ngc.nvidia.com/setup/api-key">here</a> after signing up).</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp"># &lt;xx.xx&gt; is the yy:mm for the publishing tag for NVIDIA&#39;s Pytorch</span>
<span class="cp"># container; eg. 24.08</span>
<span class="cp"># NOTE: Use the publishing tag for both the PyTorch container and the Triton Containers</span>

<span class="n">docker</span><span class="w"> </span><span class="n">run</span><span class="w"> </span><span class="o">-</span><span class="n">it</span><span class="w"> </span><span class="o">--</span><span class="n">gpus</span><span class="w"> </span><span class="n">all</span><span class="w"> </span><span class="o">-</span><span class="n">v</span><span class="w"> </span><span class="n">$</span><span class="p">{</span><span class="n">PWD</span><span class="p">}</span><span class="o">:/</span><span class="n">scratch_space</span><span class="w"> </span><span class="n">nvcr</span><span class="p">.</span><span class="n">io</span><span class="o">/</span><span class="n">nvidia</span><span class="o">/</span><span class="n">pytorch</span><span class="o">:&lt;</span><span class="n">xx</span><span class="p">.</span><span class="n">xx</span><span class="o">&gt;-</span><span class="n">py3</span>
<span class="n">cd</span><span class="w"> </span><span class="o">/</span><span class="n">scratch_space</span>
</pre></div>
</div>
<p>With the container we can export the model in to the correct directory in our Triton model repository. This export script uses the <strong>Dynamo</strong> frontend for Torch-TensorRT to compile the PyTorch model to TensorRT. Then we save the model using <strong>TorchScript</strong> as a serialization format which is supported by Triton.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>import torch
import torch_tensorrt as torchtrt
import torchvision

import torch
import torch_tensorrt
torch.hub._validate_not_a_forked_repo=lambda a,b,c: True

# load model
model = torch.hub.load(&#39;pytorch/vision:v0.10.0&#39;, &#39;resnet50&#39;, pretrained=True).eval().to(&quot;cuda&quot;)

# Compile with Torch TensorRT;
trt_model = torch_tensorrt.compile(model,
    inputs= [torch_tensorrt.Input((1, 3, 224, 224))],
    enabled_precisions= {torch_tensorrt.dtype.f16}
)

ts_trt_model = torch.jit.trace(trt_model, torch.rand(1, 3, 224, 224).to(&quot;cuda&quot;))

# Save the model
torch.jit.save(ts_trt_model, &quot;/triton_example/model_repository/resnet50/1/model.pt&quot;)
</pre></div>
</div>
<p>You can run the script with the following command (from <code class="docutils literal notranslate"><span class="pre">//examples/triton</span></code>)</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">docker</span><span class="w"> </span><span class="n">run</span><span class="w"> </span><span class="o">--</span><span class="n">gpus</span><span class="w"> </span><span class="n">all</span><span class="w"> </span><span class="o">-</span><span class="n">it</span><span class="w"> </span><span class="o">--</span><span class="n">rm</span><span class="w"> </span><span class="o">-</span><span class="n">v</span><span class="w"> </span><span class="n">$</span><span class="p">{</span><span class="n">PWD</span><span class="p">}</span><span class="o">:/</span><span class="n">triton_example</span><span class="w"> </span><span class="n">nvcr</span><span class="p">.</span><span class="n">io</span><span class="o">/</span><span class="n">nvidia</span><span class="o">/</span><span class="n">pytorch</span><span class="o">:</span><span class="n">YY</span><span class="p">.</span><span class="n">XX</span><span class="o">-</span><span class="n">py3</span><span class="w"> </span><span class="n">python</span><span class="w"> </span><span class="o">/</span><span class="n">triton_example</span><span class="o">/</span><span class="k">export</span><span class="p">.</span><span class="n">py</span>
</pre></div>
</div>
<p>This will save the serialized TorchScript version of the ResNet model in the right directory in the model repository.</p>
</section>
<section id="step-2-set-up-triton-inference-server">
<h2>Step 2: Set Up Triton Inference Server<a class="headerlink" href="#step-2-set-up-triton-inference-server" title="Permalink to this heading">¶</a></h2>
<p>If you are new to the Triton Inference Server and want to learn more, we
highly recommend to checking our <a class="reference external" href="https://github.com/triton-inference-server">Github
Repository</a>.</p>
<p>To use Triton, we need to make a model repository. A model repository, as the
name suggests, is a repository of the models the Inference server hosts. While
Triton can serve models from multiple repositories, in this example, we will
discuss the simplest possible form of the model repository.</p>
<p>The structure of this repository should look something like this:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">model_repository</span>
<span class="o">|</span>
<span class="o">+--</span><span class="w"> </span><span class="n">resnet50</span>
<span class="w">    </span><span class="o">|</span>
<span class="w">    </span><span class="o">+--</span><span class="w"> </span><span class="n">config</span><span class="p">.</span><span class="n">pbtxt</span>
<span class="w">    </span><span class="o">+--</span><span class="w"> </span><span class="mi">1</span>
<span class="w">        </span><span class="o">|</span>
<span class="w">        </span><span class="o">+--</span><span class="w"> </span><span class="n">model</span><span class="p">.</span><span class="n">pt</span>
</pre></div>
</div>
<p>There are two files that Triton requires to serve the model: the model itself
and a model configuration file which is typically provided in <code class="docutils literal notranslate"><span class="pre">config.pbtxt</span></code>.
For the model we prepared in step 1, the following configuration can be used:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="nl">name</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;resnet50&quot;</span>
<span class="nl">backend</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;pytorch&quot;</span>
<span class="nl">max_batch_size</span><span class="w"> </span><span class="p">:</span><span class="w"> </span><span class="mi">0</span>
<span class="n">input</span><span class="w"> </span><span class="p">[</span>
<span class="w">  </span><span class="p">{</span>
<span class="w">    </span><span class="nl">name</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;x&quot;</span>
<span class="w">    </span><span class="nl">data_type</span><span class="p">:</span><span class="w"> </span><span class="n">TYPE_FP32</span>
<span class="w">    </span><span class="nl">dims</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="w"> </span><span class="p">]</span>
<span class="w">  </span><span class="p">}</span>
<span class="p">]</span>
<span class="n">output</span><span class="w"> </span><span class="p">[</span>
<span class="w">  </span><span class="p">{</span>
<span class="w">    </span><span class="nl">name</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;output0&quot;</span>
<span class="w">    </span><span class="nl">data_type</span><span class="p">:</span><span class="w"> </span><span class="n">TYPE_FP32</span>
<span class="w">    </span><span class="nl">dims</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1000</span><span class="p">]</span>
<span class="w">  </span><span class="p">}</span>
<span class="p">]</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">config.pbtxt</span></code> file is used to describe the exact model configuration
with details like the names and shapes of the input and output layer(s),
datatypes, scheduling and batching details and more. If you are new to Triton,
we highly encourage you to check out this <a class="reference external" href="https://github.com/triton-inference-server/server/blob/main/docs/model_configuration.md">section of our
documentation</a>
for more details.</p>
<p>With the model repository setup, we can proceed to launch the Triton server
with the docker command below. Refer <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver">this page</a> for the pull tag for the container.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp"># Make sure that the TensorRT version in the Triton container</span>
<span class="cp"># and TensorRT version in the environment used to optimize the model</span>
<span class="cp"># are the same. Roughly, like publishing tags should have the same TensorRT version</span>

<span class="n">docker</span><span class="w"> </span><span class="n">run</span><span class="w"> </span><span class="o">--</span><span class="n">gpus</span><span class="w"> </span><span class="n">all</span><span class="w"> </span><span class="o">--</span><span class="n">rm</span><span class="w"> </span><span class="o">-</span><span class="n">p</span><span class="w"> </span><span class="mi">8000</span><span class="o">:</span><span class="mi">8000</span><span class="w"> </span><span class="o">-</span><span class="n">p</span><span class="w"> </span><span class="mi">8001</span><span class="o">:</span><span class="mi">8001</span><span class="w"> </span><span class="o">-</span><span class="n">p</span><span class="w"> </span><span class="mi">8002</span><span class="o">:</span><span class="mi">8002</span><span class="w"> </span><span class="o">-</span><span class="n">v</span><span class="w"> </span><span class="n">$</span><span class="p">{</span><span class="n">PWD</span><span class="p">}</span><span class="o">:/</span><span class="n">triton_example</span><span class="w"> </span><span class="n">nvcr</span><span class="p">.</span><span class="n">io</span><span class="o">/</span><span class="n">nvidia</span><span class="o">/</span><span class="n">tritonserver</span><span class="o">:</span><span class="n">YY</span><span class="p">.</span><span class="n">MM</span><span class="o">-</span><span class="n">py3</span><span class="w"> </span><span class="n">tritonserver</span><span class="w"> </span><span class="o">--</span><span class="n">model</span><span class="o">-</span><span class="n">repository</span><span class="o">=/</span><span class="n">triton_example</span><span class="o">/</span><span class="n">model_repository</span>
</pre></div>
</div>
<p>This should spin up a Triton Inference server. Next step, building a simple
http client to query the server.</p>
</section>
<section id="step-3-building-a-triton-client-to-query-the-server">
<h2>Step 3: Building a Triton Client to Query the Server<a class="headerlink" href="#step-3-building-a-triton-client-to-query-the-server" title="Permalink to this heading">¶</a></h2>
<p>Before proceeding, make sure to have a sample image on hand. If you don’t
have one, download an example image to test inference. In this section, we
will be going over a very basic client. For a variety of more fleshed out
examples, refer to the <a class="reference external" href="https://github.com/triton-inference-server/client/tree/main/src/python/examples">Triton Client Repository</a></p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">wget</span><span class="w">  </span><span class="o">-</span><span class="n">O</span><span class="w"> </span><span class="n">img1</span><span class="p">.</span><span class="n">jpg</span><span class="w"> </span><span class="s">&quot;https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg&quot;</span>
</pre></div>
</div>
<p>We then need to install dependencies for building a python client. These will
change from client to client. For a full list of all languages supported by Triton,
please refer to <a class="reference external" href="https://github.com/triton-inference-server/client">Triton’s client repository</a>.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">torchvision</span>
<span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">attrdict</span>
<span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">nvidia</span><span class="o">-</span><span class="n">pyindex</span>
<span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">tritonclient</span><span class="p">[</span><span class="n">all</span><span class="p">]</span>
</pre></div>
</div>
<p>Let’s jump into the client. Firstly, we write a small preprocessing function to
resize and normalize the query image.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">import</span><span class="w"> </span><span class="n">numpy</span><span class="w"> </span><span class="n">as</span><span class="w"> </span><span class="n">np</span>
<span class="n">from</span><span class="w"> </span><span class="n">torchvision</span><span class="w"> </span><span class="k">import</span><span class="w"> </span><span class="n">transforms</span>
<span class="n">from</span><span class="w"> </span><span class="n">PIL</span><span class="w"> </span><span class="k">import</span><span class="w"> </span><span class="n">Image</span>
<span class="k">import</span><span class="w"> </span><span class="n">tritonclient</span><span class="p">.</span><span class="n">http</span><span class="w"> </span><span class="n">as</span><span class="w"> </span><span class="n">httpclient</span>
<span class="n">from</span><span class="w"> </span><span class="n">tritonclient</span><span class="p">.</span><span class="n">utils</span><span class="w"> </span><span class="k">import</span><span class="w"> </span><span class="n">triton_to_np_dtype</span>

<span class="cp"># preprocessing function</span>
<span class="n">def</span><span class="w"> </span><span class="n">rn50_preprocess</span><span class="p">(</span><span class="n">img_path</span><span class="o">=</span><span class="s">&quot;/triton_example/img1.jpg&quot;</span><span class="p">)</span><span class="o">:</span>
<span class="w">  </span><span class="n">img</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Image</span><span class="p">.</span><span class="n">open</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span>
<span class="w">  </span><span class="n">preprocess</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">transforms</span><span class="p">.</span><span class="n">Compose</span><span class="p">(</span>
<span class="w">      </span><span class="p">[</span>
<span class="w">          </span><span class="n">transforms</span><span class="p">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span>
<span class="w">          </span><span class="n">transforms</span><span class="p">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
<span class="w">          </span><span class="n">transforms</span><span class="p">.</span><span class="n">ToTensor</span><span class="p">(),</span>
<span class="w">          </span><span class="n">transforms</span><span class="p">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span><span class="w"> </span><span class="mf">0.456</span><span class="p">,</span><span class="w"> </span><span class="mf">0.406</span><span class="p">],</span><span class="w"> </span><span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span><span class="w"> </span><span class="mf">0.224</span><span class="p">,</span><span class="w"> </span><span class="mf">0.225</span><span class="p">]),</span>
<span class="w">      </span><span class="p">]</span>
<span class="w">  </span><span class="p">)</span>
<span class="w">  </span><span class="k">return</span><span class="w"> </span><span class="n">preprocess</span><span class="p">(</span><span class="n">img</span><span class="p">).</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">).</span><span class="n">numpy</span><span class="p">()</span>

<span class="w"> </span><span class="n">transformed_img</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">rn50_preprocess</span><span class="p">()</span>
</pre></div>
</div>
<p>Building a client requires three basic points. Firstly, we setup a connection
with the Triton Inference Server.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp"># Setting up client</span>
<span class="n">client</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">httpclient</span><span class="p">.</span><span class="n">InferenceServerClient</span><span class="p">(</span><span class="n">url</span><span class="o">=</span><span class="s">&quot;localhost:8000&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p>Secondly, we specify the names of the input and output layer(s) of our model. This can be obtained during export and should already be specified in your <code class="docutils literal notranslate"><span class="pre">config.pbtxt</span></code></p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">inputs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">httpclient</span><span class="p">.</span><span class="n">InferInput</span><span class="p">(</span><span class="s">&quot;x&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">transformed_img</span><span class="p">.</span><span class="n">shape</span><span class="p">,</span><span class="w"> </span><span class="n">datatype</span><span class="o">=</span><span class="s">&quot;FP32&quot;</span><span class="p">)</span>
<span class="n">inputs</span><span class="p">.</span><span class="n">set_data_from_numpy</span><span class="p">(</span><span class="n">transformed_img</span><span class="p">,</span><span class="w"> </span><span class="n">binary_data</span><span class="o">=</span><span class="n">True</span><span class="p">)</span>

<span class="n">outputs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">httpclient</span><span class="p">.</span><span class="n">InferRequestedOutput</span><span class="p">(</span><span class="s">&quot;output0&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">binary_data</span><span class="o">=</span><span class="n">True</span><span class="p">,</span><span class="w"> </span><span class="n">class_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
</pre></div>
</div>
<p>Lastly, we send an inference request to the Triton Inference Server.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span># Querying the server
results = client.infer(model_name=&quot;resnet50&quot;, inputs=[inputs], outputs=[outputs])
inference_output = results.as_numpy(&#39;output0&#39;)
print(inference_output[:5])
</pre></div>
</div>
<p>The output should look like below:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>[b&#39;12.468750:90&#39; b&#39;11.523438:92&#39; b&#39;9.664062:14&#39; b&#39;8.429688:136&#39;
 b&#39;8.234375:11&#39;]
</pre></div>
</div>
<p>The output format here is <code class="docutils literal notranslate"><span class="pre">&lt;confidence_score&gt;:&lt;classification_index&gt;</span></code>.
To learn how to map these to the label names and more, refer to Triton Inference Server’s
<a class="reference external" href="https://github.com/triton-inference-server/server/blob/main/docs/protocol/extension_classification.md">documentation</a>.</p>
<p>You can try out this client quickly using</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp"># Remember to use the same publishing tag for all steps (e.g. 24.08)</span>

<span class="n">docker</span><span class="w"> </span><span class="n">run</span><span class="w"> </span><span class="o">-</span><span class="n">it</span><span class="w"> </span><span class="o">--</span><span class="n">net</span><span class="o">=</span><span class="n">host</span><span class="w"> </span><span class="o">-</span><span class="n">v</span><span class="w"> </span><span class="n">$</span><span class="p">{</span><span class="n">PWD</span><span class="p">}</span><span class="o">:/</span><span class="n">triton_example</span><span class="w"> </span><span class="n">nvcr</span><span class="p">.</span><span class="n">io</span><span class="o">/</span><span class="n">nvidia</span><span class="o">/</span><span class="n">tritonserver</span><span class="o">:</span><span class="n">YY</span><span class="p">.</span><span class="n">MM</span><span class="o">-</span><span class="n">py3</span><span class="o">-</span><span class="n">sdk</span><span class="w"> </span><span class="n">bash</span><span class="w"> </span><span class="o">-</span><span class="n">c</span><span class="w"> </span><span class="s">&quot;pip install torchvision &amp;&amp; python /triton_example/client.py&quot;</span>
</pre></div>
</div>
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